IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i8d10.1007_s10845-020-01551-8.html
   My bibliography  Save this article

Design optimization by integrating limited simulation data and shape engineering knowledge with Bayesian optimization (BO-DK4DO)

Author

Listed:
  • Jia Hao

    (Beijing Institute of Technology)

  • Mengying Zhou

    (Beijing Institute of Technology)

  • Guoxin Wang

    (Beijing Institute of Technology)

  • Liangyue Jia

    (Beijing Institute of Technology)

  • Yan Yan

    (Beijing Institute of Technology)

Abstract

Surrogate models have been widely studied for optimization tasks in the domain of engineering design. However, the expensive and time-consuming simulation cycles needed for complex products always result in limited simulation data, which brings a challenge for building high accuracy surrogate models because of the incomplete information contained in the limited simulation data. Therefore, a method that builds a surrogate model and conducts design optimization by integrating limited simulation data and engineering knowledge through Bayesian optimization (BO-DK4DO) is presented. In this method, the shape engineering knowledge is considered and used as derivative information which is integrated with the limited simulation data with a Gaussian process (GP). Then the GP is updated sequentially by sampling new simulation data and the optimal design solutions are found by maximizing the GP. The aim of BO-DK4DO is to significantly reduce the required number of computer simulations for finding optimal design solutions. The BO-DK4DO is verified by using benchmark functions and an engineering design problem: hot rod rolling. In all scenarios, the BO-DK4DO shows faster convergence rate than the general Bayesian optimization without integrating engineering knowledge, which means the required amount of data is decreased.

Suggested Citation

  • Jia Hao & Mengying Zhou & Guoxin Wang & Liangyue Jia & Yan Yan, 2020. "Design optimization by integrating limited simulation data and shape engineering knowledge with Bayesian optimization (BO-DK4DO)," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2049-2067, December.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01551-8
    DOI: 10.1007/s10845-020-01551-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01551-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-020-01551-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Saurabh Pratap & Yash Daultani & M. K. Tiwari & Biswajit Mahanty, 2018. "Rule based optimization for a bulk handling port operations," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 287-311, February.
    2. Gang Du & Yi Xia & Roger J. Jiao & Xiaojie Liu, 2019. "Leader-follower joint optimization problems in product family design," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1387-1405, March.
    3. M’hammed Sahnoun & Belgacem Bettayeb & Samuel-Jean Bassetto & Michel Tollenaere, 2016. "Simulation-based optimization of sampling plans to reduce inspections while mastering the risk exposure in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1335-1349, December.
    4. James M. Calvin & Yvonne Chen & Antanas Žilinskas, 2012. "An Adaptive Univariate Global Optimization Algorithm and Its Convergence Rate for Twice Continuously Differentiable Functions," Journal of Optimization Theory and Applications, Springer, vol. 155(2), pages 628-636, November.
    5. Daniel Russo & Benjamin Van Roy, 2014. "Learning to Optimize via Posterior Sampling," Mathematics of Operations Research, INFORMS, vol. 39(4), pages 1221-1243, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
    2. Patrick Link & Miltiadis Poursanidis & Jochen Schmid & Rebekka Zache & Martin Kurnatowski & Uwe Teicher & Steffen Ihlenfeldt, 2022. "Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2129-2142, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Simchi-Levi & Rui Sun & Huanan Zhang, 2022. "Online Learning and Optimization for Revenue Management Problems with Add-on Discounts," Management Science, INFORMS, vol. 68(10), pages 7402-7421, October.
    2. Rong Jin & David Simchi-Levi & Li Wang & Xinshang Wang & Sen Yang, 2021. "Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses," Management Science, INFORMS, vol. 67(8), pages 4756-4771, August.
    3. Mengying Zhu & Xiaolin Zheng & Yan Wang & Yuyuan Li & Qianqiao Liang, 2019. "Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling," Papers 1911.05309, arXiv.org, revised Nov 2019.
    4. Boumallessa, Zeineb & Chouikhi, Houssam & Elleuch, Mounir & Bentaher, Hatem, 2023. "Modeling and optimizing the maintenance schedule using dynamic quality and machine condition monitors in an unreliable single production system," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Anand Kalvit & Aleksandrs Slivkins & Yonatan Gur, 2024. "Incentivized Exploration via Filtered Posterior Sampling," Papers 2402.13338, arXiv.org.
    6. Dimitris Bertsimas & Velibor V. Mišić, 2016. "Decomposable Markov Decision Processes: A Fluid Optimization Approach," Operations Research, INFORMS, vol. 64(6), pages 1537-1555, December.
    7. Guy Aridor & Yishay Mansour & Aleksandrs Slivkins & Zhiwei Steven Wu, 2020. "Competing Bandits: The Perils of Exploration Under Competition," Papers 2007.10144, arXiv.org, revised Oct 2024.
    8. Po-Yi Liu & Chi-Hua Wang & Henghsiu Tsai, 2022. "Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing," Papers 2208.09372, arXiv.org, revised Sep 2022.
    9. Ye Chen & Ilya O. Ryzhov, 2020. "Technical Note—Consistency Analysis of Sequential Learning Under Approximate Bayesian Inference," Operations Research, INFORMS, vol. 68(1), pages 295-307, January.
    10. Bin Han & Ilya O. Ryzhov & Boris Defourny, 2016. "Optimal Learning in Linear Regression with Combinatorial Feature Selection," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 721-735, November.
    11. Zheng Wen & Benjamin Van Roy, 2017. "Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization," Mathematics of Operations Research, INFORMS, vol. 42(3), pages 762-782, August.
    12. Yaroslav D. Sergeyev & Marat S. Mukhametzhanov & Dmitri E. Kvasov & Daniela Lera, 2016. "Derivative-Free Local Tuning and Local Improvement Techniques Embedded in the Univariate Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 171(1), pages 186-208, October.
    13. Dhirendra Prajapati & Yash Daultani & Naoufel Cheikhrouhou & Saurabh Pratap, 2020. "Identification and ranking of key factors impacting efficiency of Indian shipping logistics sector," OPSEARCH, Springer;Operational Research Society of India, vol. 57(3), pages 765-786, September.
    14. Nabil Kahalé, 2019. "Efficient Simulation of High Dimensional Gaussian Vectors," Mathematics of Operations Research, INFORMS, vol. 44(1), pages 58-73, February.
    15. Michael Jong Kim, 2020. "Variance Regularization in Sequential Bayesian Optimization," Mathematics of Operations Research, INFORMS, vol. 45(3), pages 966-992, August.
    16. Xiangyu Gao & Huanan Zhang, 2022. "An efficient learning framework for multiproduct inventory systems with customer choices," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2492-2516, June.
    17. Powell, Warren B., 2019. "A unified framework for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 275(3), pages 795-821.
    18. Sergeyev, Yaroslav D. & Kvasov, Dmitri E. & Mukhametzhanov, Marat S., 2017. "Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 141(C), pages 96-109.
    19. Wang Chi Cheung & David Simchi-Levi & Ruihao Zhu, 2022. "Hedging the Drift: Learning to Optimize Under Nonstationarity," Management Science, INFORMS, vol. 68(3), pages 1696-1713, March.
    20. Ye Chen & Ilya O. Ryzhov, 2023. "Balancing Optimal Large Deviations in Sequential Selection," Management Science, INFORMS, vol. 69(6), pages 3457-3473, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01551-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.